The topic recommender consists of a topic ranker, and a topic database that is constructed by collecting popular topics and related comments and discussions from high-quality Internet forums, such as Instagram in the United States and douban.com in China. The topic database is updated periodically. When topic shifting is triggered, a list of topic candidates is retrieved from the database using the on-going dialog as query. The top-ranked candidate topic is chosen as the new topic. The topic ranker is implemented as a boosted tree [1]} that uses the following features:
